1. Field
The following description relates to an apparatus for classifying data using a boost pooling neutral network, and a boost pooling neural network training method therefor.
2. Description of Related Art
There are various methods of classifying data through machine learning. Among them, a method of classifying data using a neural network is one example. An ensemble method has been extensively studied as an algorithm of a hierarchical concept that improves an algorithm for classifying data using a neural network in the fields of statistics and machine learning. The ensemble method representatively includes a bagging method or a boosting method.
In the bagging method, various sets of data are configured by repeatedly performing the sampling of the data randomly, and an estimation result of the model is determined through a voting method by training various models. In a case in which each of the models shows a higher accuracy than 0.5, such a bagging method may increase a final accuracy more as more models are used. In addition, in the boosting method, models are sequentially trained and the expected result is linearly combined. After a first classifier is trained, a second classifier is trained by putting a weight value on data that the first classifier cannot classify well, and the results of the two classifiers are added together in a linear combination to obtain the least amount of error. The boosting method obtains a result by combining various classifier models trained through these processes, and the classification accuracy increases as the number of models increases, which is known to be more effective than the bagging method.
However, in the ensemble method, various models must be trained, and in the boosting method, the various models must be sequentially trained, thus increasing the training time proportionally to the number of models.